diff --git a/.github/workflows/test-universal-hap-kfp.yml b/.github/workflows/test-universal-hap-kfp.yml
new file mode 100644
index 000000000..4e52e0535
--- /dev/null
+++ b/.github/workflows/test-universal-hap-kfp.yml
@@ -0,0 +1,116 @@
+#
+# DO NOT EDIT THIS FILE: it is generated from test-transform.template,  Edit there and run make to change these files
+#
+name: Test KFP - transforms/universal/hap
+
+on:
+    workflow_dispatch:
+    push:
+        branches:
+            - "dev"
+            - "releases/**"
+        tags:
+            - "*"
+        paths:
+            - ".make.*"
+            - "transforms/.make.workflows"
+            - "transforms/universal/hap/**"
+            - "!kfp/**" # This is tested in separate workflow
+            - "!data-processing-lib/**" # This is tested in separate workflow
+            - "!**.md"
+            - "!**/doc/**"
+            - "!**/images/**"
+            - "!**.gitignore"
+    pull_request:
+        branches:
+            - "dev"
+            - "releases/**"
+        paths:
+            - ".make.*"
+            - "transforms/.make.workflows"
+            - "transforms/universal/hap/**"
+            - "!data-processing-lib/**" # This is tested in separate workflow
+            - "!kfp/**" # This is tested in separate workflow
+            - "!**.md"
+            - "!**/doc/**"
+            - "!**/images/**"
+            - "!**.gitignore"
+
+# taken from https://stackoverflow.com/questions/66335225/how-to-cancel-previous-runs-in-the-pr-when-you-push-new-commitsupdate-the-curre
+concurrency:
+    group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+    cancel-in-progress: true
+
+jobs:
+    test-kfp-v1:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout
+              uses: actions/checkout@v4
+            - name: Free up space in github runner
+              # Free space as indicated here : https://github.com/actions/runner-images/issues/2840#issuecomment-790492173
+              run: |
+                  df -h
+                  sudo rm -rf "/usr/local/share/boost"
+                  sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+                  sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /usr/local/share/powershell /usr/share/swift /usr/lib/jvm /usr/local/.ghcup
+                  sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true
+                  df -h
+            - name: Import environment variables
+              run: |
+                  cat scripts/k8s-setup/requirements.env >> $GITHUB_ENV
+                  echo "K8S_SETUP_SCRIPTS=$PWD/scripts/k8s-setup" >> $GITHUB_ENV
+                  echo "REPOROOT=$PWD" >> $GITHUB_ENV
+                  echo "PATH=$PATH:/tmp" >> $GITHUB_ENV
+            - name: Test V1 KFP workflow for transforms/universal/hap
+              timeout-minutes: 120
+              run: |
+                  KFP_BLACK_LIST=$(./scripts/check-workflows.sh -show-kfp-black-list)
+                  if [ -e "transforms/universal/hap/Makefile" -a -e "transforms/universal/hap/kfp_ray/Makefile" ]; then
+                     transform=$(basename "transforms/universal/hap")
+                     if echo ${KFP_BLACK_LIST} | grep -qv ${transform}; then
+                        $PWD/scripts/workflow_helper.sh install-tools
+                        $PWD/scripts/workflow_helper.sh test-workflow transforms/universal/hap   
+                     else
+                        $PWD/scripts/workflow_helper.sh build-workflow transforms/universal/hap
+                     fi              
+                  else
+                     echo "Skipping transforms/universal/hap kfp test for lack of Makefile and/or kfp_ray/Makefile"
+                   fi
+
+    test-kfp-v2:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout
+              uses: actions/checkout@v4
+            - name: Free up space in github runner
+              # Free space as indicated here : https://github.com/actions/runner-images/issues/2840#issuecomment-790492173
+              run: |
+                  df -h
+                  sudo rm -rf "/usr/local/share/boost"
+                  sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+                  sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /usr/local/share/powershell /usr/share/swift /usr/lib/jvm /usr/local/.ghcup
+                  sudo docker rmi $(docker image ls -aq) >/dev/null 2>&1 || true
+                  df -h
+            - name: Import environment variables
+              run: |
+                  cat scripts/k8s-setup/requirements.env >> $GITHUB_ENV
+                  echo "K8S_SETUP_SCRIPTS=$PWD/scripts/k8s-setup" >> $GITHUB_ENV
+                  echo "REPOROOT=$PWD" >> $GITHUB_ENV
+                  echo "PATH=$PATH:/tmp" >> $GITHUB_ENV
+                  echo "KFPv2=1" >> $GITHUB_ENV
+            - name: Test V2 KFP workflow for transforms/universal/hap
+              timeout-minutes: 120
+              run: |
+                  KFP_BLACK_LIST=$(./scripts/check-workflows.sh -show-kfp-black-list)
+                  if [ -e "transforms/universal/hap/Makefile" -a -e "transforms/universal/hap/kfp_ray/Makefile" ]; then
+                     transform=$(basename "transforms/universal/hap")
+                     if echo ${KFP_BLACK_LIST} | grep -qv ${transform}; then
+                        $PWD/scripts/workflow_helper.sh install-tools
+                        $PWD/scripts/workflow_helper.sh test-workflow transforms/universal/hap   
+                     else
+                        $PWD/scripts/workflow_helper.sh build-workflow transforms/universal/hap
+                     fi              
+                  else
+                     echo "Skipping transforms/universal/hap kfp test for lack of Makefile and/or kfp_ray/Makefile"
+                   fi
\ No newline at end of file
diff --git a/scripts/k8s-setup/populate_minio.sh b/scripts/k8s-setup/populate_minio.sh
index 3b22b37e7..793a3cf73 100755
--- a/scripts/k8s-setup/populate_minio.sh
+++ b/scripts/k8s-setup/populate_minio.sh
@@ -42,4 +42,4 @@ mc cp --recursive ${REPOROOT}/transforms/universal/noop/ray/test-data/input/ kfp
 mc cp --recursive ${REPOROOT}/transforms/universal/tokenization/ray/test-data/ds01/input/ kfp/test/tokenization/ds01/input
 mc cp --recursive ${REPOROOT}/transforms/universal/profiler/ray/test-data/input/ kfp/test/profiler/input
 mc cp --recursive ${REPOROOT}/transforms/universal/resize/ray/test-data/input/ kfp/test/resize/input
-
+mc cp --recursive ${REPOROOT}/transforms/universal/hap/ray/test-data/input/ kfp/test/hap/input
diff --git a/transforms/universal/hap/kfp_ray/Makefile b/transforms/universal/hap/kfp_ray/Makefile
new file mode 100644
index 000000000..4074b8713
--- /dev/null
+++ b/transforms/universal/hap/kfp_ray/Makefile
@@ -0,0 +1,59 @@
+REPOROOT=${CURDIR}/../../../../
+
+WORKFLOW_VENV_ACTIVATE=${REPOROOT}/transforms/venv/bin/activate
+include $(REPOROOT)/transforms/.make.workflows
+
+# Include the common configuration for this transform
+include ../transform.config
+
+SRC_DIR=${CURDIR}/../ray/
+
+PYTHON_WF := $(shell find ./ -name '*_wf.py')
+YAML_WF := $(patsubst %.py, %.yaml, ${PYTHON_WF})
+
+workflow-venv: .check_python_version ${WORKFLOW_VENV_ACTIVATE}
+
+.PHONY: clean
+clean:
+	@# Help: Clean up the virtual environment.
+	rm -rf ${REPOROOT}/transforms/venv 
+
+venv::
+
+build::
+
+setup::
+
+test::
+
+test-src::
+
+test-image::
+
+publish::
+
+image::
+
+kind-load-image::
+
+docker-load-image::
+
+docker-save-image::
+
+.PHONY: workflow-build
+workflow-build: workflow-venv
+	$(MAKE) $(YAML_WF)
+
+.PHONY: workflow-test
+workflow-test: workflow-build
+	$(MAKE) .workflows.test-pipeline TRANSFORM_SRC=${SRC_DIR} PIPELINE_FILE=hap_wf.yaml
+
+.PHONY: workflow-upload
+workflow-upload: workflow-build
+	@for file in $(YAML_WF); do \
+		$(MAKE) .workflows.upload-pipeline PIPELINE_FILE=$$file; \
+	done
+
+.PHONY: workflow-generate
+workflow-generate: workflow-venv
+	. ${WORKFLOW_VENV_ACTIVATE}  && ../../../../kfp/pipeline_generator/single-pipeline/run.sh -c `pwd`/pipeline_definitions.yaml -od  .
diff --git a/transforms/universal/hap/kfp_ray/hap_wf.py b/transforms/universal/hap/kfp_ray/hap_wf.py
new file mode 100644
index 000000000..786011d4d
--- /dev/null
+++ b/transforms/universal/hap/kfp_ray/hap_wf.py
@@ -0,0 +1,239 @@
+# (C) Copyright IBM Corp. 2024.
+# Licensed under the Apache License, Version 2.0 (the “License”);
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#  http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an “AS IS” BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+
+import kfp.compiler as compiler
+import kfp.components as comp
+import kfp.dsl as dsl
+from workflow_support.compile_utils import ONE_HOUR_SEC, ONE_WEEK_SEC, ComponentUtils
+
+
+task_image = "quay.io/dataprep1/data-prep-kit/hap-ray:latest"
+
+# the name of the job script
+EXEC_SCRIPT_NAME: str = "hap_transform_ray.py"
+
+# components
+base_kfp_image = "quay.io/dataprep1/data-prep-kit/kfp-data-processing:latest"
+
+# path to kfp component specifications files
+component_spec_path = "../../../../kfp/kfp_ray_components/"
+
+# compute execution parameters. Here different transforms might need different implementations. As
+# a result, instead of creating a component we are creating it in place here.
+def compute_exec_params_func(
+    worker_options: dict,
+    actor_options: dict,
+    data_s3_config: str,
+    data_max_files: int,
+    data_num_samples: int,
+    data_checkpointing: bool,
+    runtime_pipeline_id: str,
+    runtime_job_id: str,
+    runtime_code_location: dict,
+    model_name_or_path: str,
+    annotation_column: str,
+    doc_text_column: str,
+    inference_engine: str,
+    max_length: int,
+    batch_size: int,
+) -> dict:
+    from runtime_utils import KFPUtils
+
+    return {
+        "data_s3_config": data_s3_config,
+        "data_max_files": data_max_files,
+        "data_num_samples": data_num_samples,
+        "data_checkpointing": data_checkpointing,
+        "runtime_num_workers": KFPUtils.default_compute_execution_params(str(worker_options), str(actor_options)),
+        "runtime_worker_options": str(actor_options),
+        "runtime_pipeline_id": runtime_pipeline_id,
+        "runtime_job_id": runtime_job_id,
+        "runtime_code_location": str(runtime_code_location),
+        "model_name_or_path": model_name_or_path,
+        "annotation_column": annotation_column,
+        "doc_text_column": doc_text_column,
+        "inference_engine": inference_engine,
+        "max_length": max_length,
+        "batch_size": batch_size,
+    }
+
+
+# KFPv1 and KFP2 uses different methods to create a component from a function. KFPv1 uses the
+# `create_component_from_func` function, but it is deprecated by KFPv2 and so has a different import path.
+# KFPv2 recommends using the `@dsl.component` decorator, which doesn't exist in KFPv1. Therefore, here we use
+# this if/else statement and explicitly call the decorator.
+if os.getenv("KFPv2", "0") == "1":
+    # In KFPv2 dsl.RUN_ID_PLACEHOLDER is deprecated and cannot be used since SDK 2.5.0. On another hand we cannot create
+    # a unique string in a component (at runtime) and pass it to the `clean_up_task` of `ExitHandler`, due to
+    # https://github.com/kubeflow/pipelines/issues/10187. Therefore, meantime we use a unique string created at
+    # compilation time.
+    import uuid
+
+    compute_exec_params_op = dsl.component_decorator.component(
+        func=compute_exec_params_func, base_image=base_kfp_image
+    )
+    print(
+        "WARNING: the ray cluster name can be non-unique at runtime, please do not execute simultaneous Runs of the "
+        + "same version of the same pipeline !!!"
+    )
+    run_id = uuid.uuid4().hex
+else:
+    compute_exec_params_op = comp.create_component_from_func(func=compute_exec_params_func, base_image=base_kfp_image)
+    run_id = dsl.RUN_ID_PLACEHOLDER
+
+# create Ray cluster
+create_ray_op = comp.load_component_from_file(component_spec_path + "createRayClusterComponent.yaml")
+# execute job
+execute_ray_jobs_op = comp.load_component_from_file(component_spec_path + "executeRayJobComponent.yaml")
+# clean up Ray
+cleanup_ray_op = comp.load_component_from_file(component_spec_path + "deleteRayClusterComponent.yaml")
+
+# Task name is part of the pipeline name, the ray cluster name and the job name in DMF.
+TASK_NAME: str = "hap"
+
+
+@dsl.pipeline(
+    name=TASK_NAME + "-ray-pipeline",
+    description="Pipeline for hap task",
+)
+def hap(
+    # Ray cluster
+    ray_name: str = "hap-kfp-ray",  # name of Ray cluster
+    # Add image_pull_secret and image_pull_policy to ray workers if needed
+    ray_head_options: dict = {"cpu": 1, "memory": 4, "image": task_image},
+    ray_worker_options: dict = {
+        "replicas": 2,
+        "max_replicas": 2,
+        "min_replicas": 2,
+        "cpu": 2,
+        "memory": 4,
+        "image": task_image,
+    },
+    server_url: str = "http://kuberay-apiserver-service.kuberay.svc.cluster.local:8888",
+    # data access
+    data_s3_config: str = "{'input_folder': 'test/hap/input/', 'output_folder': 'test/hap/output/'}",
+    data_s3_access_secret: str = "s3-secret",
+    data_max_files: int = -1,
+    data_num_samples: int = -1,
+    data_checkpointing: bool = False,
+    # orchestrator
+    runtime_actor_options: dict = {"num_cpus": 0.8},
+    runtime_pipeline_id: str = "pipeline_id",
+    runtime_code_location: dict = {"github": "github", "commit_hash": "12345", "path": "path"},
+    # hap parameters
+    model_name_or_path: str = "ibm-granite/granite-guardian-hap-38m",
+    annotation_column: str = "hap_score",
+    doc_text_column: str = "contents",
+    inference_engine: str = "CPU",
+    max_length: int = 512,
+    batch_size: int = 128,
+    # additional parameters
+    additional_params: str = '{"wait_interval": 2, "wait_cluster_ready_tmout": 400, "wait_cluster_up_tmout": 300, "wait_job_ready_tmout": 400, "wait_print_tmout": 30, "http_retries": 5, "delete_cluster_delay_minutes": 0}',
+):
+    """
+    Pipeline to execute hap transform
+    :param ray_name: name of the Ray cluster
+    :param ray_head_options: head node options, containing the following:
+        cpu - number of cpus
+        memory - memory
+        image - image to use
+        image_pull_secret - image pull secret
+        tolerations - (optional) tolerations for the ray pods
+    :param ray_worker_options: worker node options (we here are using only 1 worker pool), containing the following:
+        replicas - number of replicas to create
+        max_replicas - max number of replicas
+        min_replicas - min number of replicas
+        cpu - number of cpus
+        memory - memory
+        image - image to use
+        image_pull_secret - image pull secret
+        tolerations - (optional) tolerations for the ray pods
+    :param server_url - server url
+    :param additional_params: additional (support) parameters, containing the following:
+        wait_interval - wait interval for API server, sec
+        wait_cluster_ready_tmout - time to wait for cluster ready, sec
+        wait_cluster_up_tmout - time to wait for cluster up, sec
+        wait_job_ready_tmout - time to wait for job ready, sec
+        wait_print_tmout - time between prints, sec
+        http_retries - http retries for API server calls
+    :param data_s3_access_secret - s3 access secret
+    :param data_s3_config - s3 configuration
+    :param data_max_files - max files to process
+    :param data_num_samples - num samples to process
+    :param runtime_actor_options - actor options
+    :param runtime_pipeline_id - pipeline id
+    :param runtime_code_location - code location
+    :param model_name_or_path - # HAP model path
+    :param annotation_column - # hap score for each document
+    :param doc_text_column - # The column name that contains the document text
+    :param inference_engine - # inference engine used
+    :param max_length - # inference engine used
+    :param batch_size - # batch size
+    :return: None
+    """
+    # create clean_up task
+    clean_up_task = cleanup_ray_op(
+        ray_name=ray_name, run_id=run_id, server_url=server_url, additional_params=additional_params
+    )
+    ComponentUtils.add_settings_to_component(clean_up_task, ONE_HOUR_SEC * 2)
+    # pipeline definition
+    with dsl.ExitHandler(clean_up_task):
+        # compute execution params
+        compute_exec_params = compute_exec_params_op(
+            worker_options=ray_worker_options,
+            actor_options=runtime_actor_options,
+            data_s3_config=data_s3_config,
+            data_max_files=data_max_files,
+            data_num_samples=data_num_samples,
+            data_checkpointing=data_checkpointing,
+            runtime_pipeline_id=runtime_pipeline_id,
+            runtime_job_id=run_id,
+            runtime_code_location=runtime_code_location,
+            model_name_or_path=model_name_or_path,
+            annotation_column=annotation_column,
+            doc_text_column=doc_text_column,
+            inference_engine=inference_engine,
+            max_length=max_length,
+            batch_size=batch_size,
+        )
+
+        ComponentUtils.add_settings_to_component(compute_exec_params, ONE_HOUR_SEC * 2)
+        # start Ray cluster
+        ray_cluster = create_ray_op(
+            ray_name=ray_name,
+            run_id=run_id,
+            ray_head_options=ray_head_options,
+            ray_worker_options=ray_worker_options,
+            server_url=server_url,
+            additional_params=additional_params,
+        )
+        ComponentUtils.add_settings_to_component(ray_cluster, ONE_HOUR_SEC * 2)
+        ray_cluster.after(compute_exec_params)
+
+        # Execute job
+        execute_job = execute_ray_jobs_op(
+            ray_name=ray_name,
+            run_id=run_id,
+            additional_params=additional_params,
+            exec_params=compute_exec_params.output,
+            exec_script_name=EXEC_SCRIPT_NAME,
+            server_url=server_url,
+        )
+        ComponentUtils.add_settings_to_component(execute_job, ONE_WEEK_SEC)
+        ComponentUtils.set_s3_env_vars_to_component(execute_job, data_s3_access_secret)
+        execute_job.after(ray_cluster)
+
+
+if __name__ == "__main__":
+    # Compiling the pipeline
+    compiler.Compiler().compile(hap, __file__.replace(".py", ".yaml"))
diff --git a/transforms/universal/hap/kfp_ray/pipeline_definitions.yaml b/transforms/universal/hap/kfp_ray/pipeline_definitions.yaml
new file mode 100644
index 000000000..9716bb349
--- /dev/null
+++ b/transforms/universal/hap/kfp_ray/pipeline_definitions.yaml
@@ -0,0 +1,44 @@
+pipeline_parameters:
+    name: "hap"
+    description: "Pipeline for hap task"
+    script_name: "hap_transform_ray.py"
+    prefix: ""
+    multi_s3: False
+    compute_func_name: ""
+    compute_func_import: ""
+    component_spec_path: ""
+
+pipeline_common_input_parameters_values:
+    kfp_base_image: "quay.io/dataprep1/data-prep-kit/kfp-data-processing:latest"
+    transform_image: "quay.io/dataprep1/data-prep-kit/hap-ray:latest"
+    s3_access_secret: "s3-secret"
+    image_pull_secret: ""
+    input_folder: "test/hap/input/"
+    output_folder: "test/hap/output/"
+
+pipeline_transform_input_parameters:
+    pipeline_arguments:
+        - name: "model_name_or_path"
+          type: "str"
+          value: "ibm-granite/granite-guardian-hap-38m"
+          description: "# HAP model path"
+        - name: "annotation_column"
+          type: "str"
+          value: "hap_score"
+          description: "# hap score for each document"
+        - name: "doc_text_column"
+          type: "str"
+          value: "contents"
+          description: "# The column name that contains the document text"
+        - name: "inference_engine"
+          type: "str"
+          value: "CPU"
+          description: "# inference engine used"
+        - name: max_length
+          type: "int"
+          value: 512
+          description: "# inference engine used"
+        - name: "batch_size"
+          type: "int"
+          value: 128
+          description: "# batch size"